To use Face Recognition in a project:
See the examples in the /examples folder on github for how to use each function.
You can also check the API docs for the ‘face_recognition’ module to see the possible parameters for each function.
The basic idea is that first you load an image:
import face_recognition image = face_recognition.load_image_file("your_file.jpg")
That loads the image into a numpy array. If you already have an image in a numpy array, you can skip this step.
Then you can perform operations on the image, like finding faces, identifying facial features or finding face encodings:
# Find all the faces in the image face_locations = face_recognition.face_locations(image) # Or maybe find the facial features in the image face_landmarks_list = face_recognition.face_landmarks(image) # Or you could get face encodings for each face in the image: list_of_face_encodings = face_recognition.face_encodings(image)
Face encodings can be compared against each other to see if the faces are a match. Note: Finding the encoding for a face is a bit slow, so you might want to save the results for each image in a database or cache if you need to refer back to it later.
But once you have the encodings for faces, you can compare them like this:
# results is an array of True/False telling if the unknown face matched anyone in the known_faces array results = face_recognition.compare_faces(known_face_encodings, a_single_unknown_face_encoding)
It’s that simple! Check out the examples for more details.